Adaptive Morphological Filter Research Articles

Denoising for variable density ESPI fringes in nondestructive testing by an adaptive multiscale morphological filter based on local mean.

Analysis of speckle images with variable density fringes is a challenging task when electronic speckle pattern interferometry (ESPI) is used for nondestructive testing of defects. In this paper, an adaptive multiscale morphological filter based on local mean is proposed. First, the image is segmented, and the regions are divided into different density levels using local mean. Then, the structural elements that adapt to different density levels are designed, and the proper size of the structural elements is determined by an iterative procedure. Finally, the morphological open-closing filtering is conducted, and the block edges are smoothed by averaging. The proposed method was applied to computer-simulation fringes and fringes experimentally obtained from a prefabricated defect specimen under thermal loading and then compared with the commonly used methods, i.e., discrete cosine filter, wavelet filter, Lee filter, and nonlocal mean filter. The experimental results showed that the proposed method had the best performance in terms of noise reduction and edge preservation. With the capability of noise reduction for ESPI images of variable density fringes, the proposed method will be helpful to build a quantitative relationship between fringes and defects in the cases of nonuniform deformation of speckle interferometry, such as nondestructive defect detecting, thermal structural analysis, and heterogeneous materials mechanical analysis.

Color image enhancement based on adaptive multi-scale morphological unsharpening filter

In order to overcome the problem in Retinex algorithm that it is possible to cause image detail loss by using Gauss function to estimate illumination, in this paper, we propose a color image enhancement algorithm based on multi-scale morphology unsharp method combined with Retinex. Firstly, the color image is converted from RGB space to HSV space. Then, the S component is enhanced by adaptive logarithmic transformation to improve its visual characteristics and make it consistent with human visual system. The method of multi-scale morphological unsharpening is adopted to estimate the V component, and the multi-scale weights are adaptively selected according to the features of the image. The V component is enhanced according to the Retinex principle. Finally, the H and the enhanced S and V components are recombined and reflected into RGB space to achieve the purpose of image enhancement. The experimental results showed that the algorithm is superior to the traditional SSR, MSR and MSRCR algorithms in terms of entropy, average gradient and sharpness, and outperforms those based on Gauss function estimation.

Morphological Filter-Assisted Ensemble Empirical Mode Decomposition

In the ensemble empirical mode decomposition (EEMD) algorithm, different realizations of white noise are added to the original signal as dyadic filter banks to overcome the mode mixing problems of empirical mode decomposition (EMD). However, not all the components in white noise are necessary, and the superfluous components will introduce additional mode mixing problems. To address this problem, morphological filter-assisted ensemble empirical mode decomposition (MF-EEMD) was proposed in this paper. First, a new method for determining the structuring element shape and size was proposed to improve the adaptive ability of morphological filter (MF). Then, the adaptive MF was introduced into EMD to remove the superfluous white noise components to improve the decomposition results. Based on the contributions of MF in a single EMD process, the MF-EEMD was proposed by combining EEMD with MF to suppress the mode mixing problems. Finally, an analog signal and a measured signal were used to verify the feasibility of MF-EEMD. The results show that MF-EEMD significantly mitigates the mode mixing problems and achieves a higher decomposition efficiency compared to that of EEMD.

A fault diagnosis scheme for planetary gearboxes using adaptive multi-scale morphology filter and modified hierarchical permutation entropy

Abstract The fault diagnosis of planetary gearboxes is crucial to reduce the maintenance costs and economic losses. This paper proposes a novel fault diagnosis method based on adaptive multi-scale morphological filter (AMMF) and modified hierarchical permutation entropy (MHPE) to identify the different health conditions of planetary gearboxes. In this method, AMMF is firstly adopted to remove the fault-unrelated components and enhance the fault characteristics. Second, MHPE is utilized to extract the fault features from the denoised vibration signals. Third, Laplacian score (LS) approach is employed to refine the fault features. In the end, the obtained features are fed into the binary tree support vector machine (BT-SVM) to accomplish the fault pattern identification. The proposed method is numerically and experimentally demonstrated to be able to recognize the different fault categories of planetary gearboxes.

Novel Multidimensional Feature Pattern Classification Method and Its Application to Fault Diagnosis

With the development of modern process industries, data-driven fault diagnosis methods have attracted more and more attention. In this paper, a novel nonlinear fault diagnosis method based on multidimensional feature pattern classification (MDFPC) is proposed. The proposed MDFPC method integrates multikernel independent component analysis (MKICA) with adaptive rank-order morphological filter (AROMF). First, some dominant independent components capturing nonlinearity are extracted from the historical process data using the MKICA algorithm, getting the template signal and the testing signal of each fault pattern. Then, a multidimensional signal classification method based on AROMF is developed to achieve the diagnosis of fault patterns. The effectiveness of the proposed fault diagnosis method is demonstrated by carrying out a case study using the Tennessee Eastman process.

Adaptive morphological filters based on a multiple orientation vector field dependent on image local features

This paper addresses the formulation of adaptive morphological filters based on spatially-variant structuring elements. The adaptivity of these filters is achieved by modifying the shape and orientation of the structuring elements according to a multiple orientation vector field. This vector field is provided by means of a bank of directional openings which can take into account the possible multiple orientations of the contours in the image. After reviewing and formalizing the definition of the spatially-variant dilation, erosion, opening and closing, the proposed structuring elements are described. These spatially-variant structuring elements are based on ellipses which vary over the image domain adapting locally their orientation according to the multiple orientation vector field and their shape (the eccentricity of the ellipses) according to the distance to relevant contours of the objects. The proposed adaptive morphological filters are used on gray-level images and are compared with spatially-invariant filters, with spatially-variant filters based on a single orientation vector field, and with adaptive morphological bilateral filters. Results show that the morphological filters based on a multiple orientation vector field are more adept at enhancing and preserving structures which contains more than one orientation.

The Application of an Improved Adaptive Morphological Filter in Power Signal Denoising

Signals are interfered by complex and changeable noises in power signal acquisition. It is difficult for traditional generalized adaptive morphological filters to deal with this situation. On this basis, the author proposes an improved filtering method and constructs a new adaptive morphological filter with multi-structural elements through a new adaptive algorithm of variable step sizes. Using structural element of a variety of shapes and dimensions, the improved morphological filter with multi-structural elements is particularly suitable for the processing of power signals with complex noises. Combinations of structural elements with better filtering effect should be adopted in design as far as possible. Compared with traditional morphological filters, it is more widely used and more flexible. It is verified through simulations that the improved morphological filter is able to eliminate complex noises in power signals more effectively and guarantees the stability of output results with strong adaptability after the change of noise environment.

Adaptive Unsymmetrical Trim-Based Morphological Filter for High-Density Impulse Noise Removal

The modified decision-based unsymmetrical trimmed median filter (MDBUTMF), which is an efficient tool for restoring images corrupted with high-density impulse noise, is only effective for certain types of images. This is because the size of the selected window is fixed and some of the center pixels are replaced by a mean value of pixels in the window. To address these issues, this paper proposes an adaptive unsymmetrical trim-based morphological filter. Firstly, a strict extremum estimation approach is used, in order to decide whether the pixel to be processed belongs to a monochrome or non-monochrome area. Then, the center pixel is replaced by a median value of pixels in a window for the monochrome area. Secondly, a relaxed extremum estimation approach is used to control the size of structuring elements. Then an adaptive structuring element is obtained and the center pixel is replaced by the output of constrained morphological operators, i.e., the minimum or maximum of pixels in a trimmed structuring element. Our experimental results show that the proposed filter is more robust and practical than the MDBUTMF. Moreover, the proposed filter provides a preferable performance compared to the existing median filters and vector median filters for high-density impulse noise removal.

Fault detection method for railway wheel flat using an adaptive multiscale morphological filter

This study explores the capacity of the morphology analysis for railway wheel flat fault detection. A dynamic model of vehicle systems with 56 degrees of freedom was set up along with a wheel flat model to calculate the dynamic responses of axle box. The vehicle axle box vibration signal is complicated because it not only contains the information of wheel defect, but also includes track condition information. Thus, how to extract the influential features of wheels from strong background noise effectively is a typical key issue for railway wheel fault detection. In this paper, an algorithm for adaptive multiscale morphological filtering (AMMF) was proposed, and its effect was evaluated by a simulated signal. And then this algorithm was employed to study the axle box vibration caused by wheel flats, as well as the influence of track irregularity and vehicle running speed on diagnosis results. Finally, the effectiveness of the proposed method was verified by bench testing. Research results demonstrate that the AMMF extracts the influential characteristic of axle box vibration signals effectively and can diagnose wheel flat faults in real time.

A Morphological Hessian Based Approach for Retinal Blood Vessels Segmentation and Denoising Using Region Based Otsu Thresholding.

Diabetic Retinopathy (DR) harm retinal blood vessels in the eye causing visual deficiency. The appearance and structure of blood vessels in retinal images play an essential part in the diagnoses of an eye sicknesses. We proposed a less computational unsupervised automated technique with promising results for detection of retinal vasculature by using morphological hessian based approach and region based Otsu thresholding. Contrast Limited Adaptive Histogram Equalization (CLAHE) and morphological filters have been used for enhancement and to remove low frequency noise or geometrical objects, respectively. The hessian matrix and eigenvalues approach used has been in a modified form at two different scales to extract wide and thin vessel enhanced images separately. Otsu thresholding has been further applied in a novel way to classify vessel and non-vessel pixels from both enhanced images. Finally, postprocessing steps has been used to eliminate the unwanted region/segment, non-vessel pixels, disease abnormalities and noise, to obtain a final segmented image. The proposed technique has been analyzed on the openly accessible DRIVE (Digital Retinal Images for Vessel Extraction) and STARE (STructured Analysis of the REtina) databases along with the ground truth data that has been precisely marked by the experts.

Adaptive Morphological Filtering Method for Structural Fusion Restoration of Hyperspectral Images

Recovering hyperspectral image (HSI) from mixed noise degradation is a challenging and promising theme in remote sensing, particularly when stripes and deadlines exist in several contiguous bands. This paper proposes a HSI's restoration method making use of adaptive morphological filtering (AMF) and fusing structure information of an auxiliary color image. An adaptive structuring element (ASE) indicating morphological features of each pixel is generated through information fusion, to simultaneously remove the mixed noise and preserve fine spatial structures. This key technology contains three main steps. First, edges are extracted from the auxiliary image exploiting its color information; then, an edge-constraint growing algorithm is used to generate the clustering kernel; finally, the ASE is obtained via goal-guided k-means clustering. The ASE has extensive application value, for it can be an enhancing module for most filters-based restoration methods, to mitigate the structural damage due to the fixed mask. Among these methods, Gaussian filter for preprocessing and majority voting for postprocessing are introduced in this paper as representatives. In addition, the auxiliary image can be both visible image of multisensor and false RGB component of the undamaged bands of the HSI, so it is relatively available. Experiments on simulated and real data sets show obvious effects on denoising and destriping both subjectively and objectively. The advantage of ASE on structure details preserving, compared to conventional approaches, is clearly demonstrated. The application value of the proposed restoration frame and ASE is further proved through the decision-level postprocessing experiments.

Character recognition in degraded document images using morphological and phase-only filtering

Nowadays, new optical character recognition (OCR) algorithms for mobile devices are actively developed. Most of these algorithms work well with documents digitized under perfect conditions. When using mobile devices, document images are subject to various distortions, such as nonuniform and poor illumination, geometric distortions, low resolution, and sensor noise. In this paper, we propose a reliable method for recognition of contextually unrelated characters in degraded document images with the use of a bank of adaptive morphological and phase-only filters. With the help of computer simulation, the results of operation of traditional and proposed approaches to detection and classification of characters of the Latin alphabet in distorted document images are compared.

The design of composite adaptive morphological filter and applications to Rician noise reduction in MR images

ABSTRACTComposite filters based on morphological operators are getting considerably attractive to medical image denoising. Most of such composite filters depend on classical morphological operators. In this article, an optimal composite adaptive morphological filter (FCAMF) is developed through a genetic programming (GP) training algorithm by using new nonlocal amoeba morphological operators. On one hand, we propose a novel method for formulating and implementing nonlocal amoeba structuring elements (SEs) for input‐adaptive morphological operators. The nonlocal amoeba SEs in the proposed strategy is divided into two parts: one is the patch distance based amoeba center, and another is the geodesic distance based amoeba boundary, by which the nonlocal patch distance and local geodesic distance are both taken into consideration. On the other hand, GP as a supervised learning algorithm is employed for building the FCAMF. In GP module, FCAMF is evolved through evaluating the fitness of several individuals over certain number of generations. The proposed method does not need any prior information about the Rician noise variance. Experimental results on both standard simulated and real MRI data sets show that the proposed filter produces excellent results and outperforms existing state‐of‐the‐art filters, especially for highly noisy image. © 2015 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 25, 15–23, 2015

Bridge detection in light detecting and ranging data based on morphological filter and skeleton extraction

An automatic approach for detecting bridges over water from light detection and ranging (LiDAR) data based on adaptive morphological filter and skeleton extraction is presented. It is inspired by data-driven and inference-based methods in machine learning. First, the three-dimensional characteristics of LiDAR data are considered in our algorithm. We design an adaptive morphological filter to classify the data into two classes, ground points and nonground points. Second, the elevation feature is used to extract the river. In this way, the search space can be greatly reduced. Third, the river is represented as a skeleton line by the morphological thinning algorithm. This concise representation makes the proposed approach more efficient to detect bridges. Finally, we propose the shortest distance rule based on the skeleton line. The fusion of the classification map and the rule is used to detect bridges. The flexibility of the proposed method is demonstrated by experiments on several different scenes. The experimental results show that the proposed approach has good performance in detecting a bridge over water.

Decision-based adaptive morphological filter for fixed-value impulse noise removal

A novel adaptive switching morphological filter for removing fixed-value impulse noise is proposed. The proposed filter firstly identifies noise pixels using the two-stage morphological noise detector, in which the initial noise detection is used to identify the noise candidates based on the morphological gradients and the refined noise detection based on the combined conditional morphological operators is adopted to further classify the noise candidates as the noise pixels or noise-free pixels. Then the detected noise pixels are removed by the adaptive morphological filter using the conditional rank-order morphological operators while the noise-free pixels are left unaltered. Extensive simulations show that the proposed filter outperforms a number of existing switching-based filters because of its excellent performance in terms of noise detection and image restoration.

Fine logarithmic adaptive soft morphological algorithm for synthetic aperture radar image segmentation

Synthetic aperture radar (SAR) appropriate image processing in conjunction with noise reduction is crucial in proper image segmentation. The authors present a new algorithm, logarithmic adaptive soft morphological (LASM) filter, utilising collectivity and flexibility of order-statistic soft morphological filters. This method not only reduces the speckle noise of the single-look SAR imagery considerably, but it significantly enhances the segmentation results. To verify the performance, a simulated SAR image is first created by applying an imagery method to an original noiseless image. The resulting image has characteristics identical to a real SAR image. The LASM method, as well as several well-known and state-of-the-art filtering algorithms, is then applied to the image to filter out the speckles. The LASM filtered results are then compared with other results by evaluating them against the original image. Some of these methods are also applied to a real SAR image and the results are also compared. Segmentation is then applied to the filtered images and the results are used as a deterministic evaluation metric. The results show marked improvement using the LASM processing the amplitude images compared with well-known methods, whereas the superiority of LASM algorithm is even more pronounced in processing the intensity images.

Restoring a silhouette of the hand in the problem of recognizing gestures by adaptive morphological filtering of a binary image

Algorithms are presented for the adaptive processing of binary images of silhouettes of the human hand obtained by means of color-brightness filters. These algorithms are based on the use of a combination of elementary morphological operations that take into account the direction of the fingers. Algorithms are presented for removing noise on binary images that are adapted to the result of the operation of a color filter, and a method is presented for filling internal contours of a silhouette of the hand in order to remove grouped marking errors. The experimental results show that the proposed image-processing method increases the probability of successful detection, tracking of the hand, and recognition of gestures.

Broadband noise suppression and feature identification of ECG waveforms using mathematical morphology and embedding theorem

The paper presents an adaptive morphological filter developed using multiscale mathematical morphology (MM) to reject broadband noise from ECG signals without affecting the feature waveforms. As a pre-processing procedure, the adaptive morphological filter cleans an ECG signal to prepare it for further analysis. The noiseless ECG signal is embedded within a two-dimensional phase space to form a binary image and the identification of the feature waveforms is carried out based on the information presented by the image. The classification of the feature waveforms is implemented by an adaptive clustering technique according to the geometric information represented by the image in the phase space. Simulation studies on ECG records from the MIT-BIH and BIDMC databases have demonstrated the effectiveness and accuracy of the proposed methods.

Partial discharge location using a hybrid transformer winding model with morphology-based noise removal

This paper employs a hybrid model of a power transformer winding for accurate partial discharge (PD) location. The hybrid model greatly reduces the computation complexity of the multi-conductor transmission line (MTL) model, yet retains the ability to accurately represent a winding. Having obtained the transfer functions of the hybrid model, PD signals can be calculated from both line-end and neutral-end at a set of detecting points. Subsequently, the pairs of the calculated signals are compared and a PD is located at the point where the minimum normalized Euclidean distance or F-ratio of the two signals is detected. In order to enhance computation efficiency a two-round technique, which includes a coarse round and a precise round, is developed and a guideline for section division is given. The influence of noise introduced by measurement devices has also been considered. To reduce the noise meanwhile preserve the feature waveforms, an adaptive morphological filter is employed. Simulation studies have shown that it is accurate and feasible to perform PD location using the hybrid model even with the presence of Gaussian noise.

A Complete Processing Chain for Shadow Detection and Reconstruction in VHR Images

The presence of shadows in very high resolution (VHR) images can represent a serious obstacle for their full exploitation. This paper proposes to face this problem as a whole through the proposal of a complete processing chain, which relies on various advanced image processing and pattern recognition tools. The first key point of the chain is that shadow areas are not only detected but also classified to allow their customized compensation. The detection and classification tasks are implemented by means of the state-of-the-art support vector machine approach. A quality check mechanism is integrated in order to reduce subsequent misreconstruction problems. The reconstruction is based on a linear regression method to compensate shadow regions by adjusting the intensities of the shaded pixels according to the statistical characteristics of the corresponding nonshadow regions. Moreover, borders are explicitly handled by making use of adaptive morphological filters and linear interpolation for the prevention of possible border artifacts in the reconstructed image. Experimental results obtained on three VHR images representing different shadow conditions are reported, discussed, and compared with two other reconstruction techniques.